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Record W2785446012 · doi:10.2166/wh.2018.261

Drinking and recreational water exposures among Canadians: Foodbook Study 2014–2015

2018· article· en· W2785446012 on OpenAlexaffabout
Rachelle Janicki, M. Kate Thomas, Katarina Pintar, Manon Fleury, Andrea Nesbitt

Bibliographic record

VenueJournal of Water and Health · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFecal contamination and water quality
Canadian institutionsUniversity of GuelphPublic Health Agency of Canada
Fundersnot available
KeywordsRecreationEnvironmental healthPublic healthBottled waterWater sourcePopulationWater qualityGeographyWaterborne diseasesSocioeconomicsHousehold incomeMedicineEnvironmental scienceWater resource managementEnvironmental engineeringEcology

Abstract

fetched live from OpenAlex

In Canada, over 400,000 enteric diseases related to drinking water occur each year, highlighting the importance of understanding sources of Canadians' drinking and recreational water exposures. To address this need, a population-based telephone survey of 10,942 Canadians was conducted between 2014 and 2015, assessing Canadian's drinking water sources and recreational water exposures using a seven-day recall method. Results were analyzed by province/territory, season, age group, gender, income, education, and urban/rural status. Store-bought bottled water was reported by nearly 20% of survey respondents as their primary drinking water source, while approximately 11% of respondents reported private well. The proportion of private well users was significantly greater than the national average in the Maritime Provinces where approximately 40-56% of respondents reported this as their primary drinking water source. As expected, Canadians' recreational water activities and exposures (e.g., swimming, pool, lake, and waterpark) peaked during summer and were most commonly reported among children aged 0-9 years. Waterborne disease in Canada requires a multi-faceted public health approach. Canadian baseline data on water exposures can inform policy and public health strategies (e.g., recreational water guidelines, private well water testing recommendations) and support research and risk assessment related to mitigating waterborne illness.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.302
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2018
Admission routes2
Has abstractyes

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